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/nw-por-review-criteria

Review dimensions and bug patterns for journey artifact reviews

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$ npx -y skills add nWave-ai/nWave --skill nw-por-review-criteria --agent claude-code

SKILL.md

nw-por-review-criteria.SKILL.md
name: nw-por-review-criteria
description: Review dimensions and bug patterns for journey artifact reviews
user-invocable: false
disable-model-invocation: true

Review Criteria Skill

Domain knowledge for product-owner-reviewer (Eclipse). Covers journey coherence, emotional arcs, shared artifacts, example data quality, CLI UX patterns.

Review Dimensions

For an initial DISCUSS, apply these dimensions only to the requested depth. Visible unexpanded sections are valid; do not demand a fully annotated emotional arc or exhaustive branches before the human chooses to deepen them. Missing emotional evidence must stay unknown, never be invented to satisfy this checklist. The detailed checks below apply to an explicitly expanded journey review.

Journey Coherence

Validate complete flow with no gaps.

Checks: all steps start-to-goal defined | no orphan steps | no dead ends | decision branches lead somewhere | error paths guide to recovery

Severity: critical = missing main flow steps / dead ends | high = orphan steps | medium = ambiguous decisions | low = minor clarity

Emotional Arc

Validate emotional design quality.

Checks: arc defined (start/middle/end) | all steps annotated | no jarring transitions | confidence builds progressively | error states guide not frustrate

Severity: critical = no arc / major jarring transitions | high = missing key annotations | medium = confidence doesn't build | low = minor polish

Shared Artifact Tracking

Validate ${variable} sources and consistency.

Checks: all ${variables} have documented source | single source of truth | all consumers listed | integration risks assessed | validation methods specified

Severity: critical = undocumented ${variables} / multiple sources | high = missing consumers / unassessed risks | medium = incomplete validation | low = minor consumer docs

Example Data Quality

Key review skill -- analyze data for integration gaps.

Checks: realistic not generic | reveals integration dependencies | catches version mismatches | catches path inconsistencies | consistent across steps

Severity: critical = generic placeholders hide issues | high = inconsistent across steps | medium = doesn't reveal deps | low = could be more realistic

Apply: 1) trace ${version} through all steps -- same? 2) compare ${install_path} step 2 vs 3 -- match? 3) does data show actual integration points?

Generic "v1.0.0" or "/path/to/install" hides bugs. Realistic "v1.2.86" from "pyproject.toml" reveals bugs.

CLI UX Patterns

Checks: command vocabulary consistent | help available | error messages guide to resolution | progressive disclosure respected

Severity: critical = inconsistent commands | high = no error recovery guidance | medium = missing progressive disclosure | low = minor vocabulary

Four Bug Patterns

Pattern 1: Version Mismatch

Multiple version sources. Trace ${version} through all steps -- same source?

Step 1: v${version} from pyproject.toml
Step 2: v${version} from version.txt  <-- MISMATCH

Pattern 2: Hardcoded URLs

URLs without canonical source. For each URL: "where is this defined?"

Install: git+https://github.com/org/repo
<-- Where is this URL canonically defined?

Pattern 3: Path Inconsistency

Paths from different sources. Trace ${path} -- same source?

Install to: ${install_path} from config
Uninstall from: ~/.claude/agents/nw/  <-- HARDCODED

Pattern 4: Missing Commands

CLI commands without slash equivalents. Check both contexts exist.

Terminal: installed product command
Agent surface: discoverable canonical command exists

Review Output Schema

review_id: "{timestamp}"
reviewer: "nw-product-owner-reviewer (Eclipse)"
artifact_reviewed: "{file path}"

strengths:
  - strength: "{Positive aspect}"
    example: "{Specific evidence}"

issues_identified:
  journey_coherence:
    - issue: "{Description}"
      severity: "critical|high|medium|low"
      location: "{Where}"
      recommendation: "{Fix}"
  emotional_arc:
    - issue: "{Description}"
      severity: "critical|high|medium|low"
      location: "{Where}"
      recommendation: "{Fix}"
  shared_artifacts:
    - issue: "{Description}"
      severity: "critical|high|medium|low"
      artifact: "{Which ${variable}}"
      recommendation: "{Fix}"
  example_data:
    - issue: "{Description}"
      severity: "critical|high|medium|low"
      data_point: "{Which data}"
      integration_risk: "{What bug it might hide}"
      recommendation: "{Fix}"
  bug_patterns_detected:
    - pattern: "version_mismatch|hardcoded_url|path_inconsistency|missing_command"
      severity: "critical|high"
      evidence: "{Finding}"
      recommendation: "{Fix}"

recommendations:
  critical: ["{Must fix before approval}"]
  high: ["{Should fix before approval}"]
  medium: ["{Fix in next iteration}"]
  low: ["{Consider for polish}"]

approval_status: "approved|rejected_pending_revisions|conditionally_approved"
approval_conditions: "{If conditional, what must be done}"

Approval Criteria

  • **approved**: No critical, no high issues
  • **conditionally_approved**: No critical, some high addressable quickly
  • **rejected_pending_revisions**: Critical issues exist, or multiple high
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Questions about nw-por-review-criteria.

Which coding agents can use nw-por-review-criteria?
nw-por-review-criteria installs in Claude Code, Codex, Cursor, Gemini CLI, GitHub Copilot, OpenCode and Windsurf with the skills CLI, using each agent's flag shown on this page. Flowy's auto-invocation, which fires a skill when your prompt matches, runs in Claude Code only.